Sentiment analysis is one of the vital areas to evaluate customer emotions. The growing prominence of sent-iment analysis is because of social network platforms, which companies use for 360-degree consumer feedback. Companies use sentiment analysis as an automated process of recognising positive and negative emotions in online text data. By examining sentiments in social media comments and reviews, businesses can better understand how customers feel about their brands and products. Visual sentiment analysis aims to understand how images affect people in terms of evoked emotions. Companies are exposed to consumers’ images on social media by consumers, and they need image analytics for social listening and response. In this paper, we took 21 random pictures from social media to identify the visual sentiment analysis. We use the image embedding algorithm in Inception V3, and Liu Hu and Ekman Algorithm to calculate the outcomes’ polarity. Further, we used the machine learning classification algorithm to identify which model does the accurate classification of evoked emotions as happy and sad. Classification algorithms are based on the 2,048 features generated by the Inception V3 algorithm, and evoked emotions are classified accordingly.